No abstract

With the advent of globalization and the evergrowing rate of technology, the volume of production as well as ease of procuring counterfeit goods has become unprecedented. Be it food, drug or luxury items, all kinds of industrial manufacturers and distributors are now seeking greater transparency in supply chain operations with a view to deter counterfeiting. This paper introduces a decentralized Blockchain based application system (DApp) with a view to identifying counterfeit products in the supply chain system. With the rapid rise of Blockchain technology, it has become known that data recorded within Blockchain is immutable and secure. Hence, the proposed project here uses this concept to handle the transfer of ownership of products. A consumer can verify the product distribution and ownership information scanning a Quick Response (QR) code generated by the DApp for each product linked to the Blockchain.

Temperature and humidity are two of the rudimentary factors that must be controlled during egg incubation. Improper temperature and humidity levels during the incubation period often result in unwanted conditions. This paper proposes the design of an efficient Mamdani fuzzy inference system instead of the widely used Takagi-Sugeno system in this field for controlling the temperature and humidity levels of an egg incubator. Though the optimum incubation temperature and humidity levels used here are that of chicken egg, the proposed methodology is applicable to other avian species as well. The input functions have been used here as per estimated values for safe hatching using Mamdani whereas defuzzification method, Center of Area (COA), has been applied for output. From the model output, a stabilized heat from temperature level and fan speed to control the humidity level of an egg incubator can be obtained. This maximizes the hatching rate of healthy chicks under any conditions in the field.

Dhaka’s sprawled area is likely to supersede the total land area of the Dhaka city in the near future. This article combines quantitative and qualitative methods to investigate sustainability concerns that have arisen because of irregular and rapid sprawling in Dhaka. Land cover change detection reveals that since 1991, the city outskirts have seen an addition of 234 square kilometres of built-up area. Spatial metrics show the dynamic process of infill and the fragmented transformation of land covers in Dhaka, which have led to low-density, leapfrog and ribbon sprawling. The city outskirts, especially the economically advantaged regions, have been observing rapid urban densification of neighbourhoods. Field observation and interviews in 19 sprawled areas confirm that the change has been influenced by industrialization, increasing demand for housing, high cost of living in Dhaka city, growing population and lack of development control regulations. The advantage of the sprawling process is that it offers economic opportunities, contributing to poverty reduction and national economic growth. However, the abrupt and sporadic nature of this transformation puts the long term economic and environmental viability of new business activities and habitation into question. Congested housing, poor accessibility, inadequate drainage system and sanitation facilities in sprawled areas have resulted in poor liveability and created social inequality, thus impeding the way for a sustainable urban transformation of peri-urban Dhaka. This article calls for a greater acknowledgement of sustainability concerns in development control regulations and a more inclusive form of governance to deal with existing sustainability challenges for Dhaka city and its rapidly transforming peripheral region.

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings to detect COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on ultrasound-based AI studies for COVID-19 detection that use public or private lung ultrasound datasets. We surveyed articles that used publicly available lung ultrasound datasets for COVID-19 and reviewed publicly available datasets and organize ultrasound-based AI studies per dataset. We analyzed and tabulated studies in several dimensions, such as data preprocessing, AI models, cross-validation, and evaluation criteria. In total, we reviewed 42 articles, where 28 articles used public datasets, and the rest used private data. Our findings suggest that ultrasound-based AI studies for the detection of COVID-19 have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.